| Title | Spectrum Occupancy Prediction Exploiting Time and Frequency Correlations Through 2D-LSTM |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Aygül, Mehmet Ali, Nazzal, Mahmoud, Ekti, Ali Rıza, Görçin, Ali, da Costa, Daniel Benevides, Ateş, Hasan Fehmi, Arslan, Hüseyin |
| Conference Name | 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) |
| Keywords | Correlation, Deep Learning, frequency correlation, Hidden Markov models, Logic gates, Predictive Metrics, Predictive models, pubcrawl, real-world spectrum measurement, Resiliency, Scalability, spectrum occupancy prediction, Time Frequency Analysis and Security, Time-frequency Analysis, Training |
| Abstract | The identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on the previously obtained occupancies. As spectrum occupancy states are correlated over time, spectrum prediction is often cast as a predictable time-series process using classical or deep learning-based models. However, this variety of methods exploits time-domain correlation and overlooks the existing correlation over frequency. In this paper, differently from previous works, we investigate a more realistic scenario by exploiting correlation over time and frequency through a 2D-long short-term memory (LSTM) model. Extensive experimental results show a performance improvement over conventional spectrum prediction methods in terms of accuracy and computational complexity. These observations are validated over the real-world spectrum measurements, assuming a frequency range between 832-862 MHz where most of the telecom operators in Turkey have private uplink bands. |
| DOI | 10.1109/VTC2020-Spring48590.2020.9129001 |
| Citation Key | aygul_spectrum_2020 |
Spectrum Occupancy Prediction Exploiting Time and Frequency Correlations Through 2D-LSTM